Analytics Legends — SAP Analytics Intelligence

What it can reach

From its install config and its own description; a sandbox run will confirm this later.

Checks on v1.0.6

1. Real tool

   passrules 2026-09-29.1 · 6 hours ago

2. Static scan

   failrules 2026-09-29.1 · 6 hours ago
   • criticalPairs a secret with an instruction to send data.tool:get_firm_intel.description

Install

Each app still asks you to confirm. Everything is pinned to the version checked above.

{
  "servers": {
    "sap-analytics": {
      "type": "http",
      "url": "https://analyticslegends.ai/mcp"
    }
  }
}

Tools it offers

As the server describes them. This text comes from the publisher and is shown as-is.

• search_firms

  Search the published Analytics Legends directory of SAP AI & analytics service providers — placement agencies, Big-4 and ESN practices, SAP vendors, platforms and community groups — by country, kind, declared SAP module and free text. Returns name, HQ country/city, website, careers URL and a one-line editorial claim. SAP END-CUSTOMER companies are NOT in this directory: they are a separate paid dataset, excluded here by the `is_client` FLAG — not by the `client_enterprise` kind code. The two are different columns, and where a row's flag and its kind label disagree in the SSOT it is the flag that decides what this tool serves, so read the flag's meaning into the answer and not the label's. PAGINATED: the whole matched set is reachable — pass `_meta.next_cursor` back as `cursor` with the same filters until it is null. When `query` is set, rows are ordered by how well the NAME matches it (exact, then prefix, then substring), and rows matching only the description come last; without `query` the order is the directory's own quality ranking.

• count_firms_by

  Answer a COUNTING question about the published firm directory in one call: how many organisations per country, per kind, per declared SAP module, or per SAP signal band — with the same `country`/`kind`/`module`/`query` filters `search_firms` takes, so you can count a slice as easily as the whole. Use this instead of paging `search_firms` and tallying rows: the directory holds thousands of organisations, and reading them all to produce a table of counts costs hundreds of calls and megabytes of rows for numbers Postgres computes in one scan. Every bucket is a value the directory actually stores; `value: null` is a real bucket meaning the field is unknown for those rows, and it is served rather than hidden — a country table that silently drops the rows with no country adds up to less than the population and says nothing about it.

• get_firm

  Fetch one organisation from the published directory by its database slug (`rows[].slug` from search_firms, verbatim). Returns the same public fields plus `partnerships_declared`, the count of partnerships this directory records for the firm — 0 on ~97 % of rows (re-measured 2026-08-14 on the published tranche: 96,8 %), meaning none declared here, never that the firm has no partners. Does not return the paid firm-intelligence profile, contacts, or any person.